Artificial Intelligence: The Quest for Universal Beauty Could Also Help Aging Research

Humanity has been attempting to measure and assess beauty long before anyone even knew about computers and algorithms. Surprisingly, a new technology may help to solve the ancient question that mankind has struggled to answer: what is universal beauty? And perhaps even more intriguingly, it might help us in aging research.

Today we will be taking a look at how perceptions of beauty have changed throughout history, how cultural bias effects that perception and how science is turning to artificial intelligence to find the answers to beauty and aging.

Many have tried to answer the question

Leonardo da Vinci attempted to capture the essence of beauty in his famous drawing, Vitruvian Man, through the use of geometrically equal proportions. This drawing was based on the writing of Roman architect Vitruvius in his treatise De Architectura.

According to Plato, beauty was an idea or form of which beautiful things were a consequence. He suggested that beauty was found when the sum of parts became a harmonious whole.

St. Augustine, in his work De Quantitate Animae, gives a theory of beauty based upon geometrical equality. He suggested that equilateral triangles are more beautiful than scalene triangles, that squares are more beautiful still and that circles are the most beautiful, being totally geometrically equal.

The quest for universal beauty

So, humankind’s quest to measure and quantify beauty has been the work of millennia, and we are still trying to find better ways of doing so. In the past, judgments and assessments of what beauty is have been subject to cultural bias, and one culture’s perception of beauty was different to another’s.

For example, the ancient Egyptians had very distinct ideas about what made someone beautiful. Archaeological studies have recovered a myriad of jewelry, including strings of beads, pendants, rings and all kinds of beauty items, which were definitely not reserved for women.

One can see their ideal of beauty depicted on mummy masks and statues with smooth and serene faces that had regular features and eyes emphasized by black khol makeup. It appears that both sexes of that period went to great lengths to enhance their beauty.

The ancient Greeks also had much to say about beauty, and their standards emphasized symmetry and harmony. Beautiful bodies were equally proportioned in shape, limbs, and face as evidenced in ancient Greek statues.

The ideal for women was small, slim with narrow shoulders, pronounced hips, wide thighs and small breasts. The ideal face had large almond-shaped eyes, a sharp nose, average-sized mouth and ears, and oval cheeks.

The ideal Greek man was portrayed as tall, toned and muscular with long powerful legs and tanned skin. The ideal face was high and with a broad forehead, wide eyes, a strong nose, a symmetrical shape and a strong jaw.

The Renaissance period saw the ideal of beauty change considerably from these earlier ideals. This era idealized the art and literature from the ancient civilizations of Rome and Greece but with its own unique twist.

Women of this period did not worry about a little extra weight; in fact, it was quite the opposite. The ideal beauty of the Renaissance was for a more voluptuous figure than most other periods of history. We can see from paintings from this time that they often focused on women who today would be considered plus-sized, and yet, at that time, their figures and features were considered the height of beauty.

It is very clear from even this very brief look at historical cultural perceptions of beauty that it is difficult to determine a universal standard.

I, for one, welcome our machine overlords

So, if cultural bias may be playing a significant role in what beauty is defined as, how can we possibly conclude what universal beauty is? Well, these difficult issues have not stopped the quest to find the answer, and now researchers are turning to science and technology to help them solve the mystery.Back in 2008, Natalie Popenko and Dr. Brian Wong published their paper called “Evolving Attractive Faces Using Morphing Technology and a Genetic Algorithm: A New Approach to Determining Ideal Facial Aesthetics”[1]. The researchers used digital morphing software to evolve increasingly more attractive faces over time-based on data collected from a variety of sources, including surveys with the public on Facebook, study groups, plastic surgeon consultations and professionals from the beauty industry.

Back in 2008, Natalie Popenko and Dr. Brian Wong published their paper called “Evolving Attractive Faces Using Morphing Technology and a Genetic Algorithm: A New Approach to Determining Ideal Facial Aesthetics”[1]. The researchers used digital morphing software to evolve increasingly more attractive faces over time-based on data collected from a variety of sources, including surveys with the public on Facebook, study groups, plastic surgeon consultations and professionals from the beauty industry.

They attempted to use predictive computing similar to how researchers generate climate models, only, in this case, they were seeing if, over time, an average face evolved into an ideal one. The conclusion of the study was that an average face did not constitute a beautiful face, and factors such as lip fullness, nasal width and eyebrow arch strongly correlated with the study scores for beauty. This is certainly something to consider when you look at some of the beauty products available today that enhance such features; perhaps the cosmetics industry is onto something.

Since then, there has been an increase in the use of technology in beauty assessment, with a range of apps being developed to that end, such as Map my Beauty, which uses facial recognition algorithms to assess beauty and suggests how and where to apply makeup in order to increase attractiveness.

The algorithms for the application are guided by professionals in the makeup field who act as a focus group for labeling and categorizing the database and how the algorithms assess beauty.

Of course, this still falls foul of the same problems our ancestors had, as humans are behind the scenes guiding the algorithms, and these same people are subject to cultural biases, just the same as da Vinci, Plato and St. Augustine were and as every human being is. No matter how many layers of technology are involved, there is always that human element with that bias.

So how can we create algorithms that do not suffer from the same biases?

Anastasia Georgievskaya from Youth Laboratories and leader of the MouseAge project at Lifespan.io, has been working to create a system that avoids this pitfall. Anastasia and her team previously worked on an artificial intelligence-based project called Beauty.ai which attempted to create a beauty contest judged by artificial intelligence.

Unfortunately, due to technical issues, this early system struggled to recognize the features on dark skin, where shadows are less pronounced and there was less contrast. You can see what we mean in this image below and how the AI struggled to find contrast.

The good news is that the team have been improving how the system works to overcome these technical issues. The MouseAge project is offering supporters the chance to take part in a beauty contest judged by artificial intelligence. In fact, by entering the contest you are actually helping them to refine the system even more.

Conclusion

The quest for universal beauty continues as it has for centuries, but perhaps this time with the help of increasingly sophisticated technology, we might finally find an answer to that age old question.

Perhaps, more importantly, what we learn from beauty algorithms may translate to more serious health research and, in particular, to the field of aging and biomedical gerontology.

Developing effective algorithms could have a wide range of applications, including helping to speed up preclinical animal research for eventual translation to humans, and even direct use in the clinic, where visual recognition may be used to detect the early onset of age-related diseases too subtle for the human eye to notice.

An example of the potential for A.I. in aging research is young.ai, an artificial intelligence-based system that helps determine biological age via visual recognition but combines that with a panel of user-submitted biomarkers to give an accurate picture of how well someone is aging. Such systems could be the basis for refining healthcare and longevity strategies and could open up some exciting possibilities.

As a scientific writer and a devoted advocate of healthy longevity technologies Steve has provided the community with multiple educational articles, interviews and podcasts, helping the general public to better understand aging and the means to modify its dynamics. His materials can be found at H+ Magazine, Longevity reporter, Psychology Today and Singularity Weblog. He is a co-author of the book “Aging Prevention for All” – a guide for the general public exploring evidence-based means to extend healthy life (in press).